Economic evaluation of diabetes prevention interventions in Bangladesh: A modelling study
Bibliographic record
Abstract
AIM: To model the long-term cost-effectiveness of scaling up two prevention interventions against type 2 diabetes mellitus (T2DM), i.e. community mobilisation through participatory learning and action (PLA) and mHealth mobile phone messaging, implemented in rural Bangladesh as part of the "DMagic" trial. METHODS: A health-economic Markov model of the three-arm, cluster-randomised controlled DMagic trial was developed. A cohort of individuals aged 50 years entered the model with impaired glucose tolerance (IGT). Outcomes included the costs (provider perspective), quality-adjusted life-years gained (QALY), incremental cost-effectiveness ratios (ICERs) and incidence of T2DM in a lifetime period. Deterministic and probabilistic sensitivity analyses were performed to reflect uncertainty. RESULTS: PLA yielded substantial reductions in diabetes incidence with only 25 % of the IGT population developing T2DM (versus 46 % in the control arm). The intervention was cost-effective against control with an ICER of 167 INT$ per QALY gained. The mHealth intervention revealed limited effectiveness at low cost, leading to an ICER of 189 INT$ per QALY gained. At willingness-to-pay ranges between 3 % and 45 % of Bangladesh GDP per capita, PLA demonstrated up to 90 % probability of being cost-effective. CONCLUSIONS: PLA is a low-cost, effective strategy to reduce the burden of T2DM, offering good value for money. TRIAL REGISTRATION: The DMagic trial was registered with the ISRCTN registry, number ISRCTN41083256.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".